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Blog
Selecting Patient Data Types for a Patient Data Initiative
Patient Data Initiative Playbook Blog Series, Part Six
Harvey Jenner, Principal, Real World Networks, IQVIA
David Voccola, Senior Director, Technology & Registry Strategy, Patient Insights, Experience & Registry Solutions (PIERS), IQVIA
Melissa Rittase, Strategy Lead, Patient Insights, Experience & Registry Solutions (PIERS), IQVIA
Feb 28, 2025
  • Blogs
  • Selecting Patient Data Types for a Patient Data Initiative

Selecting the right data types for a patient data initiative can be challenging. This blog simplifies theprocess by categorizing common data types, exploring formats, and helping organizations determine thebest fit for their initiatives. Understanding the key aspects of data selection is critical to ensuring thatpatient data initiatives are well-informed and effective. Read part five of this blog series.

Understanding data types

Patient data generally falls into three main categories, each sourced differently:

  • Direct Reporting: Includes patient-reported outcomes, lifestyle data, and observations from caregivers. This data type provides valuable insights into daily life and patient experiences. Traditionally captured via nonstandard tools, this data is now increasingly collected using validated methods in clinical settings, improving quality and reliability. Direct reporting data allows researchers to gain a patient-centric perspective that complements clinical data.
  • Clinical Data: Encompasses medical history, lab results, insurance claims, and treatment records. This data is essential for understanding a patient's medical background and informing healthcare decisions. Clinical data typically originates from healthcare providers and institutions but is increasingly available directly from patients through electronic health record access and patient portals. The structured nature of clinical data makes it a reliable source for tracking disease progression and treatment effectiveness.
  • Biological & Medical Technology Data: The newest category which includes genomic information, wearable device data, and sensor-generated insights. While offering rich health data, it presents challenges due to volume, complexity, and the need for sophisticated analytical tools. This data type is becoming more accessible due to advances in medical technology, but its integration requires robust computational and storage solutions. Despite the challenges, it offers a more granular look at patient health, enabling personalized medicine approaches.

Distinguishing data types and formats

While data types define the nature of the information (e.g., clinical or direct reporting), data formats determine how it is structured and stored, such as in tables, JSON files, or HL7 messages. Think of data types as book genres and formats as how books are arranged on a shelf.

Historically, patient data was constrained to single formats due to technological and interoperability limitations. This made data storage and processing simpler but restricted accessibility and usability. Today, evolving industry standards and federal mandates have driven the adoption of more sophisticated data formats like FHIR (Fast Healthcare Interoperability Resources)  and OMOP (Observational Medical Outcomes Partnership). These frameworks improve interoperability and enable more seamless data exchange between healthcare systems, facilitating research and innovation.

Understanding these distinctions is crucial when designing a patient data initiative, as selecting appropriate formats ensures that data can be efficiently processed and shared. The use of standard formats allows for better integration across research institutions, hospitals, and public health organizations.

Choosing the right data types

A successful patient data initiative should be guided by a well-defined scientific vision and stakeholder engagement while maintaining flexibility. Building a resource that addresses current healthcare challenges and adapts to future needs ensures long-term impact.

Consider these key factors when selecting data types:

  • Relevance to goals: Ensure the data aligns with your initiative’s objectives and supports future innovation. The selected data should answer key research questions and be scalable for future inquiries.
  • Data quality & availability: Clinical data is standardized and accessible, whereas direct reporting data provides valuable patient perspectives but may vary in quality. Sensor data offers high-resolution insights but requires advanced collection methods. Balancing data availability with quality is critical for maintaining the reliability of research findings.
  • Governance & compliance: Establish strong governance frameworks to manage data securely and support a wide research scope. Compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation) is essential to protecting patient privacy and ensuring ethical data use.
  • Cost considerations: Data collection, storage, processing, and security costs vary widely. Balancing scientific goals with budget constraints is crucial for sustainability. Some data types, such as unstructured clinical notes or genomic sequence data, require significant investment in technology infrastructure, while others may be more cost-effective but require additional validation steps to ensure accuracy.

Looking ahead: Technology solutions

After determining the right data types and formats, the next step is selecting technology solutions for effective collection, organization, and analysis. The increasing diversity of data sources necessitates advanced tools such as machine learning, cloud computing, and artificial intelligence to handle large datasets and derive meaningful insights. Selecting the right technology can enhance data integration, enable real-time analysis, and improve decision-making processes.

Moreover, collaboration between stakeholders—including healthcare providers, researchers, technology developers, and policymakers—will play a crucial role in advancing patient data initiatives. Effective partnerships ensure that data-driven solutions address real-world healthcare challenges and benefit patient communities.

In the final part of this series, we’ll explore the best technologies for patient data initiatives, empowering organizations to improve patient outcomes and drive meaningful research. By leveraging the right combination of data types, formats, and technologies, organizations can create sustainable and impactful patient data initiatives that support better healthcare decisions and innovation.

To learn more about how IQVIA can help you with patient data, contact us at ppa-contact@iqvia.com.

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